temporal-durable-mcp-weather-sample

# **Temporal & Durable MCP** The purpose of this repo is to demonstrate how easy it is to leverage workflows as tools for MCP servers by taking [modelcontext.io's weather example](https://modelcontextprotocol.io/quickstart/server) and making it durable by implementing the MCP tools as Temporal workflows --- ### Prerequisites: - Python3+ - `uv` (curl -LsSf https://astral.sh/uv/install.sh | sh) - Temporal [Local Setup Guide](https://learn.temporal.io/getting_started/?_gl=1*1bxho70*_gcl_au*MjE1OTM5MzU5LjE3NDUyNjc4Nzk.*_ga*MjY3ODg1NzM5LjE2ODc0NTcxOTA.*_ga_R90Q9SJD3D*czE3NDc0MDg0NTIkbzk0NyRnMCR0MTc0NzQwODQ1MiRqMCRsMCRoMA..) - [Claude for Desktop](https://claude.ai/download) --- ### Getting Started 1. First step - clone me 2. `cd temporal-durable-mcp-weather-sample` 3. `uv venv` (create virtual env) 4. `source .venv/bin/activate` 5. `uv pip install pyproject.toml` 6. Start Temporal Server: `temporal server start-dev` 7. Start Worker: `python worker.py` 8. Edit your Claude for Desktop Config: - Open Claude for Desktop - Click `Settings` in the toolbar - Click `Developer` - Click `Edit Config` (this should open up a finder window that points to a file called `claude_desktop_config.json`) - Open the file mentioned above in your editor of choice and modify it to look like the following - ```json { "mcpServers": { "weather": { "command": "uv", "args": [ "--directory", "/ABSOLUTE/PATH/TO/PARENT/FOLDER/temporal-durable-mcp-weather-sample", "run", "weather.py" ] } } } ``` - Validate that the tools exposed by the server (`get_alerts`, `get_forecast`) show up in `Search and Tools` by clicking the slider icon in the chat box of Claude for Desktop 9. Ask Claude for any weather alerts and the forecast for a particular city - it should leverage the tools. If it doesn't, try prompting it again encouraging it to use MCP tools that it has access to. ### Results Here we can see Claude is using the tools intelligently to look for weather alerts while getting me weather information:  #### What's Cool About This This demonstrates how tools in MCP can be implemented as workflows, adding durability. Per Claude: > Traditional MCP tool implementations are like having a single phone conversation - if the line cuts out at any point, you lose everything and have to start over from scratch. When an AI agent is performing complex, multi-step operations (like processing a large dataset, coordinating multiple API calls, or handling a lengthy business process), a single failure anywhere in the chain means complete failure.  >Temporal workflows act like a detailed journal that remembers exactly where you left off. Think of it as having a conversation with someone who takes meticulous notes - even if you both get interrupted and come back hours later, you can pick up exactly where you left off without missing a beat.  This demonstrates what we're calling "tools as workflows" - a tool is implemented as a Temporal Workflow, with the benefits of durability, state management, and retries built in.